Table 2. Comparing random slope IOU model for balanced data (m=1000, n=20 and k=3) simulated with weak derivative tracking .

		Random slope IOU models
		RE1	RE2	RE3
Converged	N	878	1000	1000
Opt. Iter.	Median (IQR)
	Bias (MCSE )
	Empirical SD of	0.095	0.036	0.028
	Median SE of (IQR)
	CP
	Bias (MCSE )
	Empirical SD	2.1	0.076	0.053
	Median SE (IQR)
	CP
	Bias (MCSE )
	Empirical SD	0.97	0.043	0.032
	Median SE (IQR)
	CP
	Bias (MCSE )
	Empirical SD	0.68	0.70	0.70
	Median SE (IQR)
	CP
	Bias (MCSE )
	Empirical SD	0.023	0.025	0.025
	Median SE (IQR)
	CP
	Bias (MCSE )
	Empirical SD	0.12	0.13	0.13
	Median SE (IQR)
	CP

Note: Optimization performed using NR algorithm and IOU parameterization . Over 1000 simulated datasets: number converged, median and IQR of the number of optimization iterations (Opt. Iter.) and for each variance parameter ν: bias of the estimates of ν with Monte Carlo standard error (MCSE), standard deviation of the estimates of its transformation , median and IQR of the estimated standard error (SE) of and empirical coverage percentage (CP) of the confidence interval for ν. Results reported to two significant figures.